Clustering using Particle Swarm Optimization (original) (raw)
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A particle swarm optimization approach to clustering
Expert Systems with Applications, 2011
The clustering problem has been studied by many researchers using various approaches, including tabu searching, genetic algorithms, simulated annealing, ant colonies, a hybridized approach, and artificial bee colonies. However, almost none of these approaches have employed the pure particle swarm optimization (PSO) technique. This study presents a new PSO approach to the clustering problem that is effective, robust, comparatively efficient, easy-to-tune and applicable when the number of clusters is either known or unknown. The algorithm was tested using two artificial and five real data sets. The results show that the algorithm can successfully solve both clustering problems with both known and unknown numbers of clusters.
Scope of Research on Particle Swarm Optimization Based Data Clustering
ArXiv, 2019
Optimization is nothing but a mathematical technique which finds maxima or minima of any function of concern in some realistic region. Different optimization techniques are proposed which are competing for the best solution. Particle Swarm Optimization (PSO) is a new, advanced, and most powerful optimization methodology that performs empirically well on several optimization problems. It is the extensively used Swarm Intelligence (SI) inspired optimization algorithm used for finding the global optimal solution in a multifaceted search region. Data clustering is one of the challenging real world applications that invite the eminent research works in variety of fields. Applicability of different PSO variants to data clustering is studied in the literature, and the analyzed research work shows that, PSO variants give poor results for multidimensional data. This paper describes the different challenges associated with multidimensional data clustering and scope of research on optimizing t...
An Improved Particle Swarm Optimization for Data Clustering
2012
In recent years, clustering is still a popular analysis tool for data statistics. The data structure identifying from the large-scale data has become a very important issue in the data mining problem. In this paper, an improved particle swarm optimization based on Gauss chaotic map for clustering is proposed. Gauss chaotic map adopts a random sequence with a random starting point as a parameter, and relies on this parameter to update the positions and velocities of the particles. It provides the significant chaos distribution to balance the exploration and exploitation capability for search process. This easy and fast function generates a random seed processes, and further improve the performance of PSO due to their unpredictability. In the experiments, the eight different clustering algorithms were extensively compared on six test data. The results indicate that the performance of our proposed method is significantly better than the performance of other algorithms for data clusteri...
Swarm and Evolutionary Computation, 2014
Optimization based pattern discovery has emerged as an important field in knowledge discovery and data mining (KDD), and has been used to enhance the efficiency and accuracy of clustering, classification, association rules and outlier detection. Cluster analysis, which identifies groups of similar data items in large datasets, is one of its recent beneficiaries. The increasing complexity and large amounts of data in the datasets have seen data clustering emerge as a popular focus for the application of optimization based techniques. Different optimization techniques have been applied to investigate the optimal solution for clustering problems. Swarm intelligence (SI) is one such optimization technique whose algorithms have successfully been demonstrated as solutions for different data clustering domains. In this paper we investigate the growth of literature in SI and its algorithms, particularly Particle Swarm Optimization (PSO). This paper makes two major contributions. Firstly, it provides a thorough literature overview focusing on some of the most cited techniques that have been used for PSO-based data clustering. Secondly, we analyze the reported results and highlight the performance of different techniques against contemporary clustering techniques. We also provide an brief overview of our PSO-based hierarchical clustering approach (HPSO-clustering) and compare the results with traditional hierarchical agglomerative clustering (HAC), K-means, and PSO clustering.
Data clustering is an approach for automatically finding classes, concepts, or groups of patterns. It also aims at representing large datasets by a few number of prototypes or clusters. It brings simplicity in modelling data and plays an important role in the process of knowledge discovery and data mining. Data mining tasks require fast and accurate partitioning of huge datasets, which may come with a variety of attributes or features. This imposes computational requirements on the clustering techniques. Swarm Intelligence (SI) has emerged that meets these requirements and has successfully been applied to a number of real world clustering problems. This paper looks into the use of Particle Swarm Optimization for cluster analysis. The effectiveness of Fuzzy C-means clustering provides enhanced performance and maintains more diversity in the swarm and allows the particles to be robust to trace the changing environment. Data structure identifying from the large scale data has become a very important in the data mining problems. Cluster analysis identifies groups of similar data items in large datasets which is one of its recent beneficiaries. The increasing complexity and large amounts of data in the data sets that have seen data clustering emerge as a popular focus for the application of optimization based techniques. Different optimization techniques have been applied to investigate the optimal solution for clustering problems. This paper also proposes two new approaches using PSO to cluster data. It is shown how PSO can be used to find the centroids of a user specified number of clusters.
Comparative Study of Particle Swarm Optimization based Unsupervised Clustering Techniques
2009
In order to overcome the shortcomings of traditional clustering algorithms such as local optima and sensitivity to initialization, a new Optimization technique, Particle Swarm Optimization is used in association with Unsupervised Clustering techniques in this paper. This new algorithm uses the capacity of global search in PSO algorithm and solves the problems associated with traditional clustering techniques. This merge avoids the local optima problem and increases the convergence speed. Parameters, time, distance and mean, are used to compare PSO based Fuzzy C-Means, PSO based Gustafson's-Kessel, PSO based Fuzzy K-Means with extragrades and PSO based K-Means are suitably plotted. Thus, Performance evaluation of Particle Swarm Optimization based Clustering techniques is achieved. Results of this PSO based clustering algorithm is used for remote image classification. Finally, accuracy of this image is computed along with its Kappa Coefficient.
Particle swarm optimization algorithm and its application to clustering analysis
International Conference on Networking, Sensing and Control, 2004
Clustering analysis is applied generally to pattern recognition, color quantization and image classification. It can help the user to distinguish the structure of data and simplify the complexity of data from mass information. The user can understand the implied information behind extracting these data. In real case, the distribution of information can be any size and shape. A particle swarm
AN EFFICIENT HYBRID PARTICLE SWARM OPTIMIZATION FOR DATA CLUSTERING
This paper presents an efficient hybrid method, namely fuzzy particle swarm optimization (FPSO) and fuzzy c-means (FCM) algorithms, to solve the fuzzy clustering problem, especially for large sizes. When the problem becomes large, the FCM algorithm may result in uneven distribution of data, making it difficult to find an optimal solution in reasonable amount of time. The PSO algorithm does find a good or near-optimal solution in reasonable time, but its performance was improved by seeding the initial swarm with the result of the c-means algorithm. The fuzzy c-means, PSO and FPSO algorithms are compared using the performance factors of object function value (OFV) and CPU execution time. It was ascertained that the computational times for the FPSO method outperforms the FCM and PSO method and had higher solution quality in terms of the objective function value (OFV).
Optimization of Cluster Points Using Particle Swarm Algorithm
IIIR India, 2023
"K-Means" Clustering is an unsupervised literacy technique used to solve clustering problems in information wisdom or machine literacy. Viscosity grounded DBSCAN is a clustering technique for spatial grouping of processes with noise. Since it is a non-parametric approach, it groups the points that are more tightly spaced apart given a collection of points in a certain space. DBSCAN outperforms the k-means technique and may be used to locate nonspherical clusters. K-means requires that the value of k be specified in advance, and the results of k-means might fluctuate greatly across iterations. We employ the DBSCAN technique to get around this flaw. DBSCAN algorithm uses density between the points to make clusters. Same density points make a cluster. Major problem with DBSCAN is choosing the correct parameters for clustering process. Bases on the value of parameters eps and minimum number of points the result of DBSCAN might change and might result in poor clustering results on the varying density dataset. To remove these restrictions, we can optimize the algorithm by optimize the selection of parameters. We can use the particle swarm optimization algorithm on the parameter selection so that every time while choosing the parameter for clustering we choose the best parameters. Particle swarm optimization algorithm can be applied to improve the clustering result efficiency by choosing the best value of eps and minimum number of points in the algorithm Procedure.